Recognizing Emotional State Changes Using Speech Processing

Ashrafidoost, Reza; Setayeshi, Saeed; Sharifi, Arash

doi:10.1109/ems.2016.017

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Emotion has been recognized from facial expressions using hidden markov models and deep belief networks with unweighted average recall (UAR) of about 56.36%(~) [20]. Different image types and emotions were examined for detecting expressions from the facial expressions using different classifiers such as KNN, HMM, GMM, SVM [21]. This paper [22] explains about learning significant features such as Support vector machine training, local invariant feature learning ,salient discriminative feature analysis for facial emotion recognition.…”

Section: Literature Surveymentioning

confidence: 99%

Emotion Recognition from Facial Expression using Deep Learning

2019

IJEAT

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Facial expression recognition is the part of Facial recognition which is gaining more importance and need for it increases tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use deep learning and image classification method to recognize expressions and classify the expressions according to the images. Various datasets are investigated and explored for training expression recognition model are explained in this paper. Inception Net is used for expression recognition with Kaggle (Facial Expression Recognition Challenge) and Karolinska Directed Emotional Faces datasets. Final accuracy of this expression recognition model using Inception Net v3 Model is 35%(~).

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Section: Literature Surveymentioning

confidence: 99%

Emotion Recognition from Facial Expression using Deep Learning

2019

IJEAT

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Infant Mood Prediction and Emotion Classification with Different Intelligent Models

Rosen

Tagore

Iyer

et al. 2021

2021 IEEE 18th India Council International Conference (INDICON)

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Recognition of speech emotion using custom 2D-convolution neural network deep learning algorithm

Zvarevashe¹,

Olugbara²

2020

IDA

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Speech emotion recognition has become the heart of most human computer interaction applications in the modern world. The growing need to develop emotionally intelligent devices has opened up a lot of research opportunities. Most researchers in this field have applied the use of handcrafted features and machine learning techniques in recognising speech emotion. However, these techniques require extra processing steps and handcrafted features are usually not robust. They are computationally intensive because the curse of dimensionality results in low discriminating power. Research has shown that deep learning algorithms are effective for extracting robust and salient features in dataset. In this study, we have developed a custom 2D-convolution neural network that performs both feature extraction and classification of vocal utterances. The neural network has been evaluated against deep multilayer perceptron neural network and deep radial basis function neural network using the Berlin database of emotional speech, Ryerson audio-visual emotional speech database and Surrey audio-visual expressed emotion corpus. The described deep learning algorithm achieves the highest precision, recall and F1-scores when compared to other existing algorithms. It is observed that there may be need to develop customized solutions for different language settings depending on the area of applications.

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Recognizing Emotional State Changes Using Speech Processing

Cited by 3 publications

References 16 publications

Emotion Recognition from Facial Expression using Deep Learning

Emotion Recognition from Facial Expression using Deep Learning

Infant Mood Prediction and Emotion Classification with Different Intelligent Models

Recognition of speech emotion using custom 2D-convolution neural network deep learning algorithm

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